Updates anomaly detection job terminology in Stack Overview (#444)

Author: lcawl

docs/en/stack/ml/anomaly-detection/api-quickref.asciidoc

@@ -2,15 +2,15 @@
 [[ml-api-quickref]]
 == API quick reference
 
-All {ml} endpoints have the following base:
+All {ml} {anomaly-detect} endpoints have the following base:
 
 [source,js]
 ----
 /_ml/
 ----
 // NOTCONSOLE
 
-The main {ml} resources can be accessed with a variety of endpoints:
+The main resources can be accessed with a variety of endpoints:
 
 * {ref}/ml-apis.html#ml-api-job-endpoint[+/anomaly_detectors/+]: Create and manage {anomaly-jobs}
 * {ref}/ml-apis.html#ml-api-calendar-endpoint[+/calendars/+]: Create and manage calendars and scheduled events
@@ -19,4 +19,4 @@ The main {ml} resources can be accessed with a variety of endpoints:
 * {ref}/ml-apis.html#ml-api-result-endpoint[+/results/+]: Access the results of an {anomaly-job}
 * {ref}/ml-apis.html#ml-api-snapshot-endpoint[+/model_snapshots/+]: Manage model snapshots
 
-For a full list, see {ref}/ml-apis.html[Machine learning APIs].
+For a full list, see {ref}/ml-apis.html[{ml-cap} {anomaly-detect} APIs].

docs/en/stack/ml/anomaly-detection/buckets.asciidoc

@@ -8,20 +8,18 @@
 The {ml-features} use the concept of a _bucket_ to divide the time series
 into batches for processing.
 
-The _bucket span_ is part of the configuration information for a job. It defines
-the time interval that is used to summarize and model the data. This is
-typically between 5 minutes to 1 hour and it depends on your data characteristics.
-When you set the bucket span, take into account the granularity at which you
-want to analyze, the frequency of the input data, the typical duration of the
-anomalies, and the frequency at which alerting is required.
+The _bucket span_ is part of the configuration information for an {anomaly-job}.
+It defines the time interval that is used to summarize and model the data. This
+is typically between 5 minutes to 1 hour and it depends on your data
+characteristics. When you set the bucket span, take into account the granularity
+at which you want to analyze, the frequency of the input data, the typical
+duration of the anomalies, and the frequency at which alerting is required.
 
 When you view your {ml} results, each bucket has an anomaly score. This score is
 a statistically aggregated and normalized view of the combined anomalousness of
 all the record results in the bucket. 
 
-In 6.5 and later releases, the {ml} analytics enhance the anomaly score for each
-bucket by considering
-//TBD: preceding?
+The {ml} analytics enhance the anomaly score for each bucket by considering
 contiguous buckets. This extra _multi-bucket analysis_ effectively uses a
 sliding window to evaluate the events in each bucket relative to the larger
 context of recent events. When you review your {ml} results, there is a 
@@ -37,9 +35,9 @@ In this example, you can see that some of the anomalies fall within the shaded
 blue area, which represents the bounds for the expected values. The bounds are
 calculated per bucket, but multi-bucket analysis is not limited by that scope.
 
-If you have more than one job, you can
-also obtain overall bucket results, which combine and correlate anomalies from
-multiple jobs into an overall score. When you view the results for job groups
-in {kib}, it provides the overall bucket scores. For more information, see
+If you have more than one {anomaly-job}, you can also obtain overall bucket
+results, which combine and correlate anomalies from multiple jobs into an
+overall score. When you view the results for job groups in {kib}, it provides
+the overall bucket scores. For more information, see
 {ref}/ml-results-resource.html[Results resources] and
 {ref}/ml-get-overall-buckets.html[Get overall buckets API].

docs/en/stack/ml/anomaly-detection/calendars.asciidoc

@@ -8,17 +8,18 @@ identify these events in advance, no anomalies are generated during that period.
 The {ml} model is not ill-affected and you do not receive spurious results.
 
 You can create calendars and scheduled events in the **Settings** pane on the
-**Machine Learning** page in {kib} or by using {ref}/ml-apis.html[{ml} APIs].
+**Machine Learning** page in {kib} or by using
+{ref}/ml-apis.html[{ml-cap} {anomaly-detect} APIs].
 
 A scheduled event must have a start time, end time, and description. In general,
 scheduled events are short in duration (typically lasting from a few hours to a
 day) and occur infrequently. If you have regularly occurring events, such as
 weekly maintenance periods, you do not need to create scheduled events for these
 circumstances; they are already handled by the {ml} analytics.
 
-You can identify zero or more scheduled events in a calendar. Jobs can then
-subscribe to calendars and the {ml} analytics handle all subsequent scheduled
-events appropriately.
+You can identify zero or more scheduled events in a calendar. {anomaly-jobs-cap}
+can then subscribe to calendars and the {ml} analytics handle all subsequent
+scheduled events appropriately.
 
 If you want to add multiple scheduled events at once, you can import an
 iCalendar (`.ics`) file in {kib} or a JSON file in the
@@ -27,13 +28,13 @@ iCalendar (`.ics`) file in {kib} or a JSON file in the
 [NOTE]
 --
 
-* You must identify scheduled events before your job analyzes the data for that
-time period. Machine learning results are not updated retroactively.
+* You must identify scheduled events before your {anomaly-job} analyzes the data
+for that time period. Machine learning results are not updated retroactively.
 * If your iCalendar file contains recurring events, only the first occurrence is
 imported.
 * Bucket results are generated during scheduled events but they have an
 anomaly score of zero. For more information about bucket results, see
-{ref}/ml-results-resource.html[Results Resources].
+{ref}/ml-results-resource.html[Results resources].
 * If you use long or frequent scheduled events, it might take longer for the
 {ml} analytics to learn to model your data and some anomalous behavior might be
 missed.

docs/en/stack/ml/anomaly-detection/datafeeds.asciidoc

@@ -2,18 +2,18 @@
 [[ml-dfeeds]]
 === {dfeeds-cap}
 
-Machine learning jobs can analyze data that is stored in {es} or data that is
+{anomaly-jobs-cap} can analyze data that is stored in {es} or data that is
 sent from some other source via an API. _{dfeeds-cap}_ retrieve data from {es}
 for analysis, which is the simpler and more common scenario.
 
-If you create jobs in {kib}, you must use {dfeeds}. When you create a job, you
-select an index pattern and {kib} configures the {dfeed} for you under the
-covers. If you use {ml} APIs instead, you can create a {dfeed} by using the
-{ref}/ml-put-datafeed.html[create {dfeeds} API] after you create a job. You can
-associate only one {dfeed} with each job.
+If you create {anomaly-jobs} in {kib}, you must use {dfeeds}. When you create a
+job, you select an index pattern and {kib} configures the {dfeed} for you under
+the covers. If you use APIs instead, you can create a {dfeed} by using the
+{ref}/ml-put-datafeed.html[create {dfeeds} API] after you create an
+{anomaly-job}. You can associate only one {dfeed} with each job.
 
 For a description of all the {dfeed} properties, see
-{ref}/ml-datafeed-resource.html[Datafeed Resources].
+{ref}/ml-datafeed-resource.html[Datafeed resources].
 
 To start retrieving data from {es}, you must start the {dfeed}. When you start
 it, you can optionally specify start and end times. If you do not specify an

docs/en/stack/ml/anomaly-detection/forecasting.asciidoc

@@ -15,7 +15,8 @@ your disk utilization will reach 100% before the end of next week.
 
 Each forecast has a unique ID, which you can use to distinguish between forecasts
 that you created at different times. You can create a forecast by using the
-{ref}/ml-forecast.html[Forecast Jobs API] or by using {kib}. For example:
+{ref}/ml-forecast.html[forecast {anomaly-jobs} API] or by using {kib}. For
+example:
 
 
 [role="screenshot"]
@@ -34,19 +35,17 @@ Eventually if the confidence levels are too low, the forecast stops.
 You can also optionally specify when the forecast expires. By default, it
 expires in 14 days and is deleted automatically thereafter. You can specify a
 different expiration period by using the `expires_in` parameter in the
-{ref}/ml-forecast.html[Forecast Jobs API].
-
-//Add examples of forecast_request_stats and forecast documents?
+{ref}/ml-forecast.html[forecast {anomaly-jobs} API].
 
 There are some limitations that affect your ability to create a forecast:
 
 * You can generate only three forecasts concurrently. There is no limit to the
 number of forecasts that you retain. Existing forecasts are not overwritten when
 you create new forecasts. Rather, they are automatically deleted when they expire.
-* If you use an `over_field_name` property in your job (that is to say, it's a
-_population job_), you cannot create a forecast.
-* If you use any of the following analytical functions in your job, you
-cannot create a forecast:
+* If you use an `over_field_name` property in your {anomaly-job} (that is to say,
+it's a _population job_), you cannot create a forecast.
+* If you use any of the following analytical functions in your {anomaly-job},
+you cannot create a forecast:
 ** `lat_long`
 ** `rare` and `freq_rare`
 ** `time_of_day` and `time_of_week`
@@ -56,9 +55,10 @@ For more information about any of these functions, see <<ml-functions>>.
 --
 * Forecasts run concurrently with real-time {ml} analysis. That is to say, {ml}
 analysis does not stop while forecasts are generated. Forecasts can have an
-impact on {ml} jobs, however, especially in terms of memory usage. For this
+impact on {anomaly-jobs}, however, especially in terms of memory usage. For this
 reason, forecasts run only if the model memory status is acceptable.
-* The job must be open when you create a forecast. Otherwise, an error occurs.
+* The {anomaly-job} must be open when you create a forecast. Otherwise, an error
+occurs.
 * If there is insufficient data to generate any meaningful predictions, an
 error occurs. In general, forecasts that are created early in the learning phase
 of the data analysis are less accurate.

docs/en/stack/ml/anomaly-detection/jobs.asciidoc

@@ -1,16 +1,16 @@
 [role="xpack"]
 [[ml-jobs]]
-=== Machine learning jobs
+=== {anomaly-jobs-cap}
 ++++
 <titleabbrev>Jobs</titleabbrev>
 ++++
 
-Machine learning jobs contain the configuration information and metadata
+{anomaly-jobs-cap} contain the configuration information and metadata
 necessary to perform an analytics task.
 
-Each job has one or more _detectors_. A detector applies an analytical function
-to specific fields in your data. For more information about the types of
-analysis you can perform, see <<ml-functions>>.
+Each {anomaly-job} has one or more _detectors_. A detector applies an analytical
+function to specific fields in your data. For more information about the types
+of analysis you can perform, see <<ml-functions>>.
 
 A job can also contain properties that affect which types of entities or events
 are considered anomalous. For example, you can specify whether entities are
@@ -20,7 +20,7 @@ categories and partitions. Some of these more advanced job configurations
 are described in the following section: <<ml-configuring>>.
 
 For a description of all the job properties, see
-{ref}/ml-job-resource.html[Job Resources].
+{ref}/ml-job-resource.html[{anomaly-job-cap} resources].
 
 In {kib}, there are wizards that help you create specific types of jobs, such
 as _single metric_, _multi-metric_, and _population_ jobs. A single metric job

docs/en/stack/ml/anomaly-detection/limitations.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-limitations]]
-== Machine learning limitations
+== {ml-cap} {anomaly-detect} limitations
 [subs="attributes"]
 ++++
 <titleabbrev>Limitations</titleabbrev>

docs/en/stack/ml/anomaly-detection/rules.asciidoc

@@ -4,7 +4,7 @@
 
 By default, as described in <<ml-analyzing>>, anomaly detection is unsupervised 
 and the {ml} models have no awareness of the domain of your data. As a result, 
-{ml} jobs might identify events that are statistically significant but are 
+{anomaly-jobs} might identify events that are statistically significant but are 
 uninteresting when you know the larger context. Machine learning custom rules
 enable you to customize anomaly detection. 
 
@@ -22,7 +22,7 @@ for the rule, such that it applies only to certain machines. The scope is
 defined by using {ml} filters. 
 
 _Filters_ contain a list of values that you can use to include or exclude events 
-from the {ml} analysis. You can use the same filter in multiple jobs. 
+from the {ml} analysis. You can use the same filter in multiple {anomaly-jobs}. 
 
 If you are analyzing web traffic, you might create a filter that contains a list 
 of IP addresses. For example, maybe they are IP addresses that you trust to 

docs/en/stack/ml/anomaly-detection/troubleshooting.asciidoc

@@ -1,6 +1,6 @@
 [role="xpack"]
 [[ml-troubleshooting]]
-== Troubleshooting {ml}
+== Troubleshooting {ml} {anomaly-detect}
 ++++
 <titleabbrev>Troubleshooting</titleabbrev>
 ++++
@@ -42,7 +42,7 @@ For more information, see {ref}/rolling-upgrades.html[Rolling upgrades].
 [[ml-mappingclash]]
 === Job creation failure due to mapping clash
 
-This problem occurs when you try to create a job.
+This problem occurs when you try to create an {anomaly-job}.
 
 *Symptoms:*
 
@@ -61,15 +61,16 @@ data types or different `fields` settings.
 
 By default, {ml} results are stored in the `.ml-anomalies-shared` index in {es}.
 To resolve this issue, click *Advanced > Use dedicated index* when you create
-the job in {kib}. If you are using the create job API, specify an index name in
-the `results_index_name` property.
+the job in {kib}. If you are using the create {anomaly-job} job API, specify an
+index name in the `results_index_name` property.
 
 [[ml-jobnames]]
 === {kib} cannot display jobs with invalid characters in their name
 
-This problem occurs when you create a job by using the
-{ref}/ml-put-job.html[Create Jobs API] then try to view that job in {kib}. In
-particular, the problem occurs when you use a period(.) in the job identifier.
+This problem occurs when you create an {anomaly-job} by using the
+{ref}/ml-put-job.html[Create {anomaly-jobs} API] then try to view that job in
+{kib}. In particular, the problem occurs when you use a period(.) in the job
+identifier.
 
 *Symptoms:*
 
@@ -82,10 +83,11 @@ abbreviated name, it is displayed.
 
 *Resolution:*
 
-Create jobs in {kib} or ensure that you create jobs with valid identifiers when
-you use the {ml} APIs. For more information about valid identifiers, see
-{ref}/ml-put-job.html[Create Jobs API] or
-{ref}/ml-job-resource.html[Job Resources].
+Create {anomaly-jobs} in {kib} or ensure that you create {anomaly-jobs} with
+valid identifiers when you use the APIs. For more information about valid
+identifiers, see
+{ref}/ml-put-job.html[Create {anomaly-jobs} API] or
+{ref}/ml-job-resource.html[{anomaly-detect-cap} job resources].
 
 [[ml-upgradedf]]